VQV Signal
According to a post on X by @spheron, the GPU cloud provider Spheron reports that an NVIDIA H200 reaches 31,712 tokens per second on Llama 2 70B inference, compared with 22,290 on an H100, or about 42% more throughput. Spheron attributes the gain to the H200's faster HBM3e memory rather than additional compute. The post also links to a Spheron blog that outlines which NVIDIA GPUs suit different LLM workloads.
According to a post on X by @spheron, the GPU cloud provider Spheron reports that an NVIDIA H200 reaches 31,712 tokens per second on Llama 2 70B inference, compared with 22,290 on an H100, or about 42% more throughput. Spheron attributes the gain to the H200's faster HBM3e memory rather than additional compute. The post also links to a Spheron blog that outlines which NVIDIA GPUs suit different LLM workloads.
AI-assisted summary based on the listed source.
GPU cloud Spheron says an H200 hits 31,712 tokens per second on Llama 2 70B inference versus 22,290 on an H100, about 42% more throughput. It credits the H200's faster HBM3e memory rather than extra compute, and its blog walks through which NVIDIA GPUs fit which LLM workloads. Also:...
Teams choosing hardware for LLM inference may weigh memory bandwidth alongside raw compute when comparing H200 and H100 options. The figures come from Spheron's own claim as shared in this post, so the test setup should be checked before relying on them for purchasing or capacity decisions. Readers can compare them against other published benchmarks for the same model.
Hardware and robotics watchers may want to track whether this becomes a product, benchmark, or deployment signal.
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VQV surfaced this signal because it is recent, relevant to LLM Inference, connected to Scobleizer News Pulse (X).
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